There is increasing interest in computation circuits that operate at high speed with many inputs to provide artificial intelligence. Systems of integrated circuits of the kind particularly useful for employing associative memory to provide artificial intelligence, pattern recognition and optimization processes are now often described as neural networks. Such systems are characterized by the need for many thousands of components.
Systems of the kind needed for these tasks are still in the rudimentary stage and leave considerable room for improvement. In particular much of the effort hitherto has been on digital techniques. This increases the number of computating and processing operations because of the need to work with binary digits. Networks that operate on analog rather than digital signals offer considerable promise for more efficient operation.
Neural networks that have utilized analog processing have tended to use continuous signals because of the better availability of neural circuits for processing continuous analog signals. However, the use of discrete-time or analog samples offers greater promise for large networks because of easier simulation and control.
To this end, an object of the invention is a neural network that is made up of simple, readily available components that can readily be integrated on a large scale, and that can process analog signals on a discrete time basis.